{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Election Data Project - Polls and Donors\n",
    "In this Data Project we will be looking at data from the 2012 election.\n",
    "\n",
    "In this project we will analyze two datasets. The first data set will be the results of political polls. We will analyze this aggregated poll data and answer some questions:\n",
    "\n",
    "1. Who was being polled and what was their party affiliation?\n",
    "2. Did the poll results favor Romney or Obama?\n",
    "3. How do undecided voters effect the poll?\n",
    "4. Can we account for the undecided voters?\n",
    "5. How did voter sentiment change over time?\n",
    "6. Can we see an effect in the polls from the debates?\n",
    "\n",
    "We'll discuss the second data set later on!\n",
    "\n",
    "Let's go ahead and start with our standard imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For data\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import numpy as np\n",
    "\n",
    "# For visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data for the polls will be obtained from HuffPost Pollster. You can check their website here. There are some pretty awesome politcal data stes to play with there so I encourage you to go and mess around with it yourself after completing this project.\n",
    "\n",
    "We're going to use the requests module to import some data from the web. For more information on requests, check out the documentation here.\n",
    "\n",
    "We will also be using StringIO to work with csv data we get from HuffPost. StringIO provides a convenient means of working with text in memory using the file API, find out more about it here.\n",
    "\n",
    "Let's go and start with our standard imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = \"http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv\"\n",
    "\n",
    "source = requests.get(url).text\n",
    "\n",
    "poll_data = StringIO(source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 590 entries, 0 to 589\n",
      "Data columns (total 17 columns):\n",
      "Pollster                  590 non-null object\n",
      "Start Date                590 non-null object\n",
      "End Date                  590 non-null object\n",
      "Entry Date/Time (ET)      590 non-null object\n",
      "Number of Observations    568 non-null float64\n",
      "Population                590 non-null object\n",
      "Mode                      590 non-null object\n",
      "Undecided                 423 non-null float64\n",
      "Other                     202 non-null float64\n",
      "Obama                     590 non-null float64\n",
      "Romney                    590 non-null float64\n",
      "Pollster URL              590 non-null object\n",
      "Source URL                588 non-null object\n",
      "Partisan                  590 non-null object\n",
      "Affiliation               590 non-null object\n",
      "Question Text             0 non-null float64\n",
      "Question Iteration        590 non-null int64\n",
      "dtypes: float64(6), int64(1), object(10)\n",
      "memory usage: 83.0+ KB\n"
     ]
    }
   ],
   "source": [
    "poll_df = pd.read_csv(poll_data)\n",
    "poll_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Politico/GWU/Battleground</td>\n",
       "      <td>2012-11-04</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:40:26Z</td>\n",
       "      <td>1000</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-26T15:31:23Z</td>\n",
       "      <td>740</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>47</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T09:22:02Z</td>\n",
       "      <td>872</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>49</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster  Start Date    End Date  Entry Date/Time (ET)  \\\n",
       "0  Politico/GWU/Battleground  2012-11-04  2012-11-05  2012-11-06T08:40:26Z   \n",
       "1           YouGov/Economist  2012-11-03  2012-11-05  2012-11-26T15:31:23Z   \n",
       "2           Gravis Marketing  2012-11-03  2012-11-05  2012-11-06T09:22:02Z   \n",
       "3                   IBD/TIPP  2012-11-03  2012-11-05  2012-11-06T08:51:48Z   \n",
       "4                  Rasmussen  2012-11-03  2012-11-05  2012-11-06T08:47:50Z   \n",
       "\n",
       "   Number of Observations     Population             Mode  Undecided  Other  \\\n",
       "0                    1000  Likely Voters       Live Phone          6    NaN   \n",
       "1                     740  Likely Voters         Internet          3    NaN   \n",
       "2                     872  Likely Voters  Automated Phone          4    NaN   \n",
       "3                     712  Likely Voters       Live Phone        NaN      1   \n",
       "4                    1500  Likely Voters  Automated Phone        NaN    NaN   \n",
       "\n",
       "   Obama  Romney                                       Pollster URL  \\\n",
       "0     47      47  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1     49      47  http://elections.huffingtonpost.com/pollster/p...   \n",
       "2     48      48  http://elections.huffingtonpost.com/pollster/p...   \n",
       "3     50      49  http://elections.huffingtonpost.com/pollster/p...   \n",
       "4     48      49  http://elections.huffingtonpost.com/pollster/p...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0  http://www.politico.com/news/stories/1112/8338...  Nonpartisan        None   \n",
       "1  http://cdn.yougov.com/cumulus_uploads/document...  Nonpartisan        None   \n",
       "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None   \n",
       "3  http://news.investors.com/special-report/50841...  Nonpartisan        None   \n",
       "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None   \n",
       "\n",
       "   Question Text  Question Iteration  \n",
       "0            NaN                   1  \n",
       "1            NaN                   1  \n",
       "2            NaN                   1  \n",
       "3            NaN                   1  \n",
       "4            NaN                   1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>568.000000</td>\n",
       "      <td>423.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>590.000000</td>\n",
       "      <td>590.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1295.390845</td>\n",
       "      <td>6.546099</td>\n",
       "      <td>3.400990</td>\n",
       "      <td>46.774576</td>\n",
       "      <td>44.572881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1616.240271</td>\n",
       "      <td>3.698614</td>\n",
       "      <td>2.721318</td>\n",
       "      <td>2.447069</td>\n",
       "      <td>2.925320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>328.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>855.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36472.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of Observations   Undecided       Other       Obama      Romney  \\\n",
       "count              568.000000  423.000000  202.000000  590.000000  590.000000   \n",
       "mean              1295.390845    6.546099    3.400990   46.774576   44.572881   \n",
       "std               1616.240271    3.698614    2.721318    2.447069    2.925320   \n",
       "min                328.000000    1.000000    0.000000   37.000000   32.000000   \n",
       "25%                855.750000    4.000000    2.000000   45.000000   43.000000   \n",
       "50%               1000.000000    6.000000    3.000000   47.000000   45.000000   \n",
       "75%               1500.000000    8.000000    4.000000   48.000000   46.000000   \n",
       "max              36472.000000   28.000000   19.000000   54.000000   53.000000   \n",
       "\n",
       "       Question Text  Question Iteration  \n",
       "count              0                 590  \n",
       "mean             NaN                   1  \n",
       "std              NaN                   0  \n",
       "min              NaN                   1  \n",
       "25%              NaN                   1  \n",
       "50%              NaN                   1  \n",
       "75%              NaN                   1  \n",
       "max              NaN                   1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908c717550>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QfQENABdm5u5O/0XEb2/MiohZwLOAByPiSkb/DP4AmF+ecm5m/rLTBXeCPYSJ4ZfAa4Fj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\nVBgIkiQA/h9q0sv0qeNhXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908bfd85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Affiliation',data=poll_df,order=['Dem','None','Rep'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908c826f60>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XcYGwZMkSHnrooVOus3TpUtq1axeyS3InTpxIXFwcANu3b+exxx4LSiBA8a9JsLYhUplV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908be7d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Affiliation',data=poll_df,hue='Population',order=['Dem','None','Rep'],\n",
    "              hue_order=['Adults','Likely Voters','Likely Voters- Republican','Registered Voters'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avg = pd.DataFrame(poll_df.mean())\n",
    "\n",
    "avg.drop('Number of Observations',axis=0,inplace=True)\n",
    "avg.drop('Question Text',axis=0,inplace=True)\n",
    "avg.drop('Question Iteration',axis=0,inplace=True)\n",
    "avg.drop('Other',axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>6.546099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obama</th>\n",
       "      <td>46.774576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romney</th>\n",
       "      <td>44.572881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "Undecided   6.546099\n",
       "Obama      46.774576\n",
       "Romney     44.572881"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "std = pd.DataFrame(poll_df.std())\n",
    "\n",
    "std.drop('Number of Observations',axis=0,inplace=True)\n",
    "std.drop('Question Text',axis=0,inplace=True)\n",
    "std.drop('Question Iteration',axis=0,inplace=True)\n",
    "std.drop('Other',axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>3.698614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obama</th>\n",
       "      <td>2.447069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romney</th>\n",
       "      <td>2.925320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "Undecided  3.698614\n",
       "Obama      2.447069\n",
       "Romney     2.925320"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908cc49f60>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V4+hVBU8pHUhd+V5mvq50iJI8z7tHEfHrwBMy80MRcS3wzsz8TuFY6kJ7VcH7gN8FvpiZ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\nlrckVcjylqQK/R+YEFRRx63s8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908cafb7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg.plot(yerr=std,kind='bar',legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "poll_avg = pd.concat([avg,std],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Undecided</th>\n",
       "      <td>6.546099</td>\n",
       "      <td>3.698614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obama</th>\n",
       "      <td>46.774576</td>\n",
       "      <td>2.447069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romney</th>\n",
       "      <td>44.572881</td>\n",
       "      <td>2.925320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Average       STD\n",
       "Undecided   6.546099  3.698614\n",
       "Obama      46.774576  2.447069\n",
       "Romney     44.572881  2.925320"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_avg.columns = ['Average','STD']\n",
    "poll_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908cd24d68>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EKWcgEClnXjfuZRmaDq0ft9LsafdrBf14/o5vpB3nnwOoT7kuHA6jqamJeOVv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\nEEIooajcQwihhP4DW0E0znGIw7UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908cd2a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poll_df.plot(x='End Date',y=['Obama','Romney','Undecided'],linestyle='',marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Entry Date/Time (ET)</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Population</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Politico/GWU/Battleground</td>\n",
       "      <td>2012-11-04</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:40:26Z</td>\n",
       "      <td>1000</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-26T15:31:23Z</td>\n",
       "      <td>740</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>47</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T09:22:02Z</td>\n",
       "      <td>872</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>49</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rasmussen</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster  Start Date    End Date  Entry Date/Time (ET)  \\\n",
       "0  Politico/GWU/Battleground  2012-11-04  2012-11-05  2012-11-06T08:40:26Z   \n",
       "1           YouGov/Economist  2012-11-03  2012-11-05  2012-11-26T15:31:23Z   \n",
       "2           Gravis Marketing  2012-11-03  2012-11-05  2012-11-06T09:22:02Z   \n",
       "3                   IBD/TIPP  2012-11-03  2012-11-05  2012-11-06T08:51:48Z   \n",
       "4                  Rasmussen  2012-11-03  2012-11-05  2012-11-06T08:47:50Z   \n",
       "\n",
       "   Number of Observations     Population             Mode  Undecided  Other  \\\n",
       "0                    1000  Likely Voters       Live Phone          6    NaN   \n",
       "1                     740  Likely Voters         Internet          3    NaN   \n",
       "2                     872  Likely Voters  Automated Phone          4    NaN   \n",
       "3                     712  Likely Voters       Live Phone        NaN      1   \n",
       "4                    1500  Likely Voters  Automated Phone        NaN    NaN   \n",
       "\n",
       "   Obama  Romney                                       Pollster URL  \\\n",
       "0     47      47  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1     49      47  http://elections.huffingtonpost.com/pollster/p...   \n",
       "2     48      48  http://elections.huffingtonpost.com/pollster/p...   \n",
       "3     50      49  http://elections.huffingtonpost.com/pollster/p...   \n",
       "4     48      49  http://elections.huffingtonpost.com/pollster/p...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0  http://www.politico.com/news/stories/1112/8338...  Nonpartisan        None   \n",
       "1  http://cdn.yougov.com/cumulus_uploads/document...  Nonpartisan        None   \n",
       "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None   \n",
       "3  http://news.investors.com/special-report/50841...  Nonpartisan        None   \n",
       "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None   \n",
       "\n",
       "   Question Text  Question Iteration  Difference  \n",
       "0            NaN                   1        0.00  \n",
       "1            NaN                   1        0.02  \n",
       "2            NaN                   1        0.00  \n",
       "3            NaN                   1        0.01  \n",
       "4            NaN                   1       -0.01  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df['Difference'] = (poll_df.Obama - poll_df.Romney)/100\n",
    "\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-03-13</td>\n",
       "      <td>1403</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-04-17</td>\n",
       "      <td>686</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-06-12</td>\n",
       "      <td>638</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-15</td>\n",
       "      <td>577</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Start Date  Number of Observations  Undecided  Other  Obama  Romney  \\\n",
       "0  2009-03-13                    1403         12    NaN     44      44   \n",
       "1  2009-04-17                     686         11    NaN     50      39   \n",
       "2  2009-05-14                    1000         12    NaN     53      35   \n",
       "3  2009-06-12                     638         12    NaN     48      40   \n",
       "4  2009-07-15                     577         11    NaN     49      40   \n",
       "\n",
       "   Question Text  Question Iteration  Difference  \n",
       "0            NaN                   1        0.00  \n",
       "1            NaN                   1        0.11  \n",
       "2            NaN                   1        0.18  \n",
       "3            NaN                   1        0.08  \n",
       "4            NaN                   1        0.09  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poll_df = poll_df.groupby(['Start Date'],as_index=False).mean()\n",
    "\n",
    "poll_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908ce31940>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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X+qFmHtxmmnMasO6RdZrfr390/SiXJHsKueyc/NdM8/bFOZ3I1cyjF4CH+Wzx+/0JvYOH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\ntN/+01VahJEWc7OasuDTC+x7pHZWbUHy78eH47Rva2fKCVP47tB3ua7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ziFZFbZ+zc79+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s9XIc9XmV4K3K2UWg70AZ8CUEpdDbyltf6zY9t4pnaCcKQQ1KItkHTvKAuLm4cgCIIgGPJSprXW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\nPNF24BdkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908cdd6438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "329\n",
      "356\n"
     ]
    }
   ],
   "source": [
    "row_in = 0\n",
    "xlimit = []\n",
    "\n",
    "for date in poll_df['Start Date']:\n",
    "    if date[0:7] == '2012-10':\n",
    "        xlimit.append(row_in)\n",
    "        row_in +=1\n",
    "    else:\n",
    "        row_in +=1\n",
    "        \n",
    "print(min(xlimit))\n",
    "print(max(xlimit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2908df01908>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y1MUV56J5m29mEK1s+a623DaJ+ffP5/CWUz8bDXn9DkEetQbNwX5LvQtcZoxZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908dea90f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poll_df.plot('Start Date','Difference',figsize=(12,4),marker='o',linestyle='-',color='purple',xlim=(329,356))\n",
    "\n",
    "# October 3rd\n",
    "plt.axvline(x=329+2,linewidth=4,color='grey')\n",
    "\n",
    "# October 11th\n",
    "plt.axvline(x=329+10,linewidth=4,color='red')\n",
    "\n",
    "#October 22nd\n",
    "plt.axvline(x=329+21,linewidth=4,color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Donor Data Set\n",
    "Let's go ahead and switch gears and take a look at a data set consisting of information on donations to the federal campaign.\n",
    "\n",
    "This is going to be the biggest data set we've looked at so far. You can download it here , then make sure to save it to the same folder your iPython Notebooks are in.\n",
    "\n",
    "The questions we will be trying to answer while looking at this Data Set is:\n",
    "\n",
    "1. How much was donated and what was the average donation?\n",
    "2. How did the donations differ between candidates?\n",
    "3. How did the donations differ between Democrats and Republicans?\n",
    "4. What were the demographics of the donors?\n",
    "5. Is there a pattern to donation amounts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2723: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "donor_df = pd.read_csv('Election_Donor_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1001731 entries, 0 to 1001730\n",
      "Data columns (total 16 columns):\n",
      "cmte_id              1001731 non-null object\n",
      "cand_id              1001731 non-null object\n",
      "cand_nm              1001731 non-null object\n",
      "contbr_nm            1001731 non-null object\n",
      "contbr_city          1001712 non-null object\n",
      "contbr_st            1001727 non-null object\n",
      "contbr_zip           1001620 non-null object\n",
      "contbr_employer      988002 non-null object\n",
      "contbr_occupation    993301 non-null object\n",
      "contb_receipt_amt    1001731 non-null float64\n",
      "contb_receipt_dt     1001731 non-null object\n",
      "receipt_desc         14166 non-null object\n",
      "memo_cd              92482 non-null object\n",
      "memo_text            97770 non-null object\n",
      "form_tp              1001731 non-null object\n",
      "file_num             1001731 non-null int64\n",
      "dtypes: float64(1), int64(1), object(14)\n",
      "memory usage: 129.9+ MB\n"
     ]
    }
   ],
   "source": [
    "donor_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>LANETT</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.68633e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.24548e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250</td>\n",
       "      <td>01-AUG-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>HOT SPRINGS NATION</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.19016e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
       "0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
       "3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
       "4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
       "\n",
       "          contbr_city contbr_st   contbr_zip        contbr_employer  \\\n",
       "0              MOBILE        AL   3.6601e+08                RETIRED   \n",
       "1              MOBILE        AL   3.6601e+08                RETIRED   \n",
       "2              LANETT        AL  3.68633e+08  INFORMATION REQUESTED   \n",
       "3             PIGGOTT        AR  7.24548e+08                   NONE   \n",
       "4  HOT SPRINGS NATION        AR  7.19016e+08                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
       "0                RETIRED                250        20-JUN-11          NaN   \n",
       "1                RETIRED                 50        23-JUN-11          NaN   \n",
       "2  INFORMATION REQUESTED                250        05-JUL-11          NaN   \n",
       "3                RETIRED                250        01-AUG-11          NaN   \n",
       "4                RETIRED                300        20-JUN-11          NaN   \n",
       "\n",
       "  memo_cd memo_text form_tp  file_num  \n",
       "0     NaN       NaN   SA17A    736166  \n",
       "1     NaN       NaN   SA17A    736166  \n",
       "2     NaN       NaN   SA17A    749073  \n",
       "3     NaN       NaN   SA17A    749073  \n",
       "4     NaN       NaN   SA17A    736166  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 100.00     178188\n",
       " 50.00      137584\n",
       " 25.00      110345\n",
       " 250.00      91182\n",
       " 500.00      57984\n",
       " 2500.00     49005\n",
       " 35.00       37237\n",
       " 1000.00     36494\n",
       " 10.00       33986\n",
       " 200.00      27813\n",
       " 20.00       17565\n",
       " 15.00       16163\n",
       " 150.00      14600\n",
       " 75.00       13647\n",
       " 201.20      11718\n",
       " 30.00       11381\n",
       " 300.00      11204\n",
       " 20.12        9897\n",
       " 5.00         9024\n",
       " 40.00        5007\n",
       " 2000.00      4128\n",
       " 55.00        3760\n",
       " 1500.00      3705\n",
       " 3.00         3383\n",
       " 60.00        3084\n",
       " 400.00       3066\n",
       "-2500.00      2727\n",
       " 110.00       2554\n",
       " 125.00       2520\n",
       " 19.00        2474\n",
       "             ...  \n",
       " 128.68          1\n",
       " 60.71           1\n",
       " 139.68          1\n",
       " 143.43          1\n",
       " 155.43          1\n",
       " 160.93          1\n",
       " 190.68          1\n",
       " 195.18          1\n",
       " 198.18          1\n",
       " 62.46           1\n",
       " 93.88           1\n",
       " 101.88          1\n",
       " 34.46           1\n",
       " 25.96           1\n",
       " 26.21           1\n",
       " 28.96           1\n",
       " 29.46           1\n",
       " 30.46           1\n",
       " 31.96           1\n",
       " 33.71           1\n",
       " 36.46           1\n",
       " 54.71           1\n",
       " 41.21           1\n",
       " 98.38           1\n",
       " 46.96           1\n",
       " 47.21           1\n",
       " 96.13           1\n",
       " 95.38           1\n",
       " 50.46           1\n",
       " 248.18          1\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df['contb_receipt_amt'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average donation was 298.24 with a std 3749.67\n"
     ]
    }
   ],
   "source": [
    "don_mean = donor_df['contb_receipt_amt'].mean()\n",
    "\n",
    "don_std = donor_df['contb_receipt_amt'].std()\n",
    "\n",
    "print('The average donation was %.2f with a std %.2f'%(don_mean,don_std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:3: FutureWarning: sort is deprecated, use sort_values(inplace=True) for for INPLACE sorting\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "114604     -30800.00\n",
       "226986     -25800.00\n",
       "101356      -7500.00\n",
       "398429      -5500.00\n",
       "250737      -5455.00\n",
       "33821       -5414.31\n",
       "908565      -5115.00\n",
       "456649      -5000.00\n",
       "574657      -5000.00\n",
       "30513       -5000.00\n",
       "562267      -5000.00\n",
       "30584       -5000.00\n",
       "86268       -5000.00\n",
       "708920      -5000.00\n",
       "665887      -5000.00\n",
       "708899      -5000.00\n",
       "708929      -5000.00\n",
       "21172       -5000.00\n",
       "21168       -5000.00\n",
       "21167       -5000.00\n",
       "262328      -5000.00\n",
       "946875      -5000.00\n",
       "7361        -5000.00\n",
       "416403      -5000.00\n",
       "21164       -5000.00\n",
       "707945      -5000.00\n",
       "615101      -5000.00\n",
       "7973        -5000.00\n",
       "54430       -5000.00\n",
       "54434       -5000.00\n",
       "             ...    \n",
       "708022      10000.00\n",
       "708898      10000.00\n",
       "710177      10000.00\n",
       "876244      10000.00\n",
       "709608      10000.00\n",
       "708919      10000.00\n",
       "709739      10000.00\n",
       "91145       10000.00\n",
       "708138      10000.00\n",
       "993178      10000.00\n",
       "709813      10000.00\n",
       "710730      10000.00\n",
       "708928      10000.00\n",
       "709268      10000.00\n",
       "99829       10000.00\n",
       "90076       10000.00\n",
       "709859      10000.00\n",
       "41888       10000.00\n",
       "65131       12700.00\n",
       "834301      25000.00\n",
       "823345      25000.00\n",
       "217891      25800.00\n",
       "114754      33300.00\n",
       "257270     451726.00\n",
       "335187     512710.91\n",
       "319478     526246.17\n",
       "344419    1511192.17\n",
       "344539    1679114.65\n",
       "326651    1944042.43\n",
       "325136    2014490.51\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_donor = donor_df['contb_receipt_amt'].copy()\n",
    "\n",
    "top_donor.sort()\n",
    "\n",
    "top_donor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100     178188\n",
       "50      137584\n",
       "25      110345\n",
       "250      91182\n",
       "500      57984\n",
       "2500     49005\n",
       "35       37237\n",
       "1000     36494\n",
       "10       33986\n",
       "200      27813\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_donor = top_donor[top_donor > 0]\n",
    "top_donor.sort_values()\n",
    "top_donor.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908e1d0c88>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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La8BbNEJh4ajyN6vy2qi6Q2PUfauwfwD09fUxNDTEnj17xiwv1er2WtUPgHq9PuX9mK6c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\nEJEkFTNEJEnFDBFJUrH/BIe6tpqoGQ+eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2909f8a1668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "com_don = top_donor[top_donor < 2500]\n",
    "\n",
    "com_don.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
       "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
       "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick', 'Cain, Herman',\n",
       "       'Gingrich, Newt', 'McCotter, Thaddeus G', 'Huntsman, Jon',\n",
       "       'Perry, Rick'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates = donor_df.cand_nm.unique()\n",
    "\n",
    "candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Dictionary of party affiliation\n",
    "party_map = {'Bachmann, Michelle': 'Republican',\n",
    "           'Cain, Herman': 'Republican',\n",
    "           'Gingrich, Newt': 'Republican',\n",
    "           'Huntsman, Jon': 'Republican',\n",
    "           'Johnson, Gary Earl': 'Republican',\n",
    "           'McCotter, Thaddeus G': 'Republican',\n",
    "           'Obama, Barack': 'Democrat',\n",
    "           'Paul, Ron': 'Republican',\n",
    "           'Pawlenty, Timothy': 'Republican',\n",
    "           'Perry, Rick': 'Republican',\n",
    "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
    "           'Romney, Mitt': 'Republican',\n",
    "           'Santorum, Rick': 'Republican'}\n",
    "\n",
    "donor_df['Party'] = donor_df.cand_nm.map(party_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nfor i in range(0,len(donor_df)):\\n    if donor_df['cand_nm'][i] == 'Obama,Barack':\\n        donor_df['Party][i] = 'Democrat'\\n    else:\\n        donor_df['Party][i] = 'Republican'\\n\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "for i in range(0,len(donor_df)):\n",
    "    if donor_df['cand_nm'][i] == 'Obama,Barack':\n",
    "        donor_df['Party][i] = 'Democrat'\n",
    "    else:\n",
    "        donor_df['Party][i] = 'Republican'\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "donor_df = donor_df[donor_df.contb_receipt_amt > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "      <th>Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>LANETT</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.68633e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.24548e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250</td>\n",
       "      <td>01-AUG-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>HOT SPRINGS NATION</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.19016e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
       "0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
       "3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
       "4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
       "\n",
       "          contbr_city contbr_st   contbr_zip        contbr_employer  \\\n",
       "0              MOBILE        AL   3.6601e+08                RETIRED   \n",
       "1              MOBILE        AL   3.6601e+08                RETIRED   \n",
       "2              LANETT        AL  3.68633e+08  INFORMATION REQUESTED   \n",
       "3             PIGGOTT        AR  7.24548e+08                   NONE   \n",
       "4  HOT SPRINGS NATION        AR  7.19016e+08                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
       "0                RETIRED                250        20-JUN-11          NaN   \n",
       "1                RETIRED                 50        23-JUN-11          NaN   \n",
       "2  INFORMATION REQUESTED                250        05-JUL-11          NaN   \n",
       "3                RETIRED                250        01-AUG-11          NaN   \n",
       "4                RETIRED                300        20-JUN-11          NaN   \n",
       "\n",
       "  memo_cd memo_text form_tp  file_num       Party  \n",
       "0     NaN       NaN   SA17A    736166  Republican  \n",
       "1     NaN       NaN   SA17A    736166  Republican  \n",
       "2     NaN       NaN   SA17A    749073  Republican  \n",
       "3     NaN       NaN   SA17A    749073  Republican  \n",
       "4     NaN       NaN   SA17A    736166  Republican  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "Bachmann, Michelle                 13082\n",
       "Cain, Herman                       20052\n",
       "Gingrich, Newt                     46883\n",
       "Huntsman, Jon                       4066\n",
       "Johnson, Gary Earl                  1234\n",
       "McCotter, Thaddeus G                  73\n",
       "Obama, Barack                     589127\n",
       "Paul, Ron                         143161\n",
       "Pawlenty, Timothy                   3844\n",
       "Perry, Rick                        12709\n",
       "Roemer, Charles E. 'Buddy' III      5844\n",
       "Romney, Mitt                      105155\n",
       "Santorum, Rick                     46245\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.groupby('cand_nm')['contb_receipt_amt'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Party\n",
       "Democrat      589127\n",
       "Republican    402348\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.groupby('Party')['contb_receipt_amt'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "Bachmann, Michelle                2.711439e+06\n",
       "Cain, Herman                      7.101082e+06\n",
       "Gingrich, Newt                    1.283277e+07\n",
       "Huntsman, Jon                     3.330373e+06\n",
       "Johnson, Gary Earl                5.669616e+05\n",
       "McCotter, Thaddeus G              3.903000e+04\n",
       "Obama, Barack                     1.358774e+08\n",
       "Paul, Ron                         2.100962e+07\n",
       "Pawlenty, Timothy                 6.004819e+06\n",
       "Perry, Rick                       2.030575e+07\n",
       "Roemer, Charles E. 'Buddy' III    3.730099e+05\n",
       "Romney, Mitt                      8.833591e+07\n",
       "Santorum, Rick                    1.104316e+07\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The candidate Bachmann, Michelle raised 2711439 dollars\n",
      "\n",
      "\n",
      "The candidate Cain, Herman raised 7101082 dollars\n",
      "\n",
      "\n",
      "The candidate Gingrich, Newt raised 12832770 dollars\n",
      "\n",
      "\n",
      "The candidate Huntsman, Jon raised 3330373 dollars\n",
      "\n",
      "\n",
      "The candidate Johnson, Gary Earl raised 566962 dollars\n",
      "\n",
      "\n",
      "The candidate McCotter, Thaddeus G raised 39030 dollars\n",
      "\n",
      "\n",
      "The candidate Obama, Barack raised 135877427 dollars\n",
      "\n",
      "\n",
      "The candidate Paul, Ron raised 21009620 dollars\n",
      "\n",
      "\n",
      "The candidate Pawlenty, Timothy raised 6004819 dollars\n",
      "\n",
      "\n",
      "The candidate Perry, Rick raised 20305754 dollars\n",
      "\n",
      "\n",
      "The candidate Roemer, Charles E. 'Buddy' III raised 373010 dollars\n",
      "\n",
      "\n",
      "The candidate Romney, Mitt raised 88335908 dollars\n",
      "\n",
      "\n",
      "The candidate Santorum, Rick raised 11043159 dollars\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cand_amount = donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()\n",
    "\n",
    "i=0\n",
    "\n",
    "for don in cand_amount:\n",
    "    print('The candidate %s raised %.0f dollars'%(cand_amount.index[i],don))\n",
    "    print('\\n')\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29086a42f28>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RWuUAFJ4hVdLFkn5NWnC0FWm/y+w6rzAjonMnpNqrXSBvczRpqS6ktK8Xknnn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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908e4b50f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cand_amount.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908e52f978>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S4SxySSqcRS5JhbPIJalwFrkkFc4il6TCWeSSVDiLXJIKZ5FLUuEsckkqnEUuSYWzyCWp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908e5161d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "donor_df.groupby('Party')['contb_receipt_amt'].sum().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MIXED-MEDIA ARTIST / STORYTELLER</th>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA VICE PRESIDENT</th>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESEARCH ASSOCIATE</th>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEACHER</th>\n",
       "      <td>500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THERAPIST</th>\n",
       "      <td>3900</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Party                                Democrat  Republican\n",
       "contbr_occupation                                        \n",
       "   MIXED-MEDIA ARTIST / STORYTELLER       100         NaN\n",
       " AREA VICE PRESIDENT                      250         NaN\n",
       " RESEARCH ASSOCIATE                       100         NaN\n",
       " TEACHER                                  500         NaN\n",
       " THERAPIST                               3900         NaN"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df = donor_df.pivot_table('contb_receipt_amt',\n",
    "                                     index='contbr_occupation',\n",
    "                                     columns='Party',\n",
    "                                     aggfunc='sum')\n",
    "\n",
    "occupation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45067, 2)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "occupation_df = occupation_df[occupation_df.sum(1) > 1000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 2)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupation_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908e6199b0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IbsAZQBtwnar+ooH9dRzHcRLUFOSO4zhOz8ZrdjqO47Q4Lsgdx3FaHBfkjuM4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\nRMclkUdEdFwSeURExyWRR0R03P8DYXdiB2xwqZ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908e654898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occupation_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908e77b7f0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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StIarPjdgyZIlLFiwgI033phLLrmEBx98kGXLlnHIIYew9957M23aNLbbbjv+8Ic/AHDp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\nLQsiSZIkSbVlQSRJkiSptv4/feTtGZEXPUUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908e7459b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occupation_df.plot(kind='barh',figsize=(10,12),cmap='seismic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "occupation_df.drop(['INFORMATION REQUESTED PER BEST EFFORTS','INFORMATION REQUESTED'],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:117: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "occupation_df.loc['CEO'] = occupation_df.loc['CEO'] + occupation_df.loc['C.E.O.']\n",
    "occupation_df.loc['INVESTOR'] = occupation_df.loc['INVESTOR'] + occupation_df.loc['INVESTMENTS']\n",
    "\n",
    "occupation_df.drop(['C.E.O.','INVESTMENTS'],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2908eb96e48>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6jCVJktTNgLYFTI0Y8jG6gFMzfc2uJEmqOwPaFrBeoycgSZI0hMyhlSRJUlNz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\nA60kSZLGmgOtJEmSxpoDrSRJksbaPyNYPqs/qsncAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2908e700fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "occupation_df.plot(kind='barh',figsize=(10,12),cmap='seismic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
